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A Balanced Repeated Replication Estimator of Sampling Variance for Apparent and Predicted Species Richness

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Design-based estimators of the sampling variance for apparent (S0 = observed) and predicted species richness () have been lacking. A balanced repeated replication (BRR) estimator is proposed and recommended for S0 and . Good performance of the estimators is demonstrated in two contrasting examples with Monte Carlo (MC) simulations of simple random sampling with fixed area quadrats (plots) for estimating forest tree species richness. Chao and Lee's estimator of species richness was used for . In BRR a set of half-samples forming an orthogonal design with respect to the inclusion/exclusion of sample records is used to produce estimates of from the observed half-sample and models for the effect of data-splitting. BRR estimates of the sampling variance of S0 were close to the MC estimates in the examples from eastern Canada (PROV) and the Barro Colorado Island (BCI) and clearly superior to naive model-based estimates. BRR estimates of were generally close to their MC counterparts, but in BCI a bias of approximately −10% was seen in small samples (n ≤ 24). BRR estimates of sampling variance of in PROV were close to the MC estimates for n ≤ 60. With larger sample sizes the BRR estimates drifted toward values obtained with Chao and Lee's model-based variance estimator. In BCI the opposite was true.

Keywords: Hadamard design matrix; Monte Carlo simulation; forest inventory; quadrat sampling; singleton species; species occurrence

Document Type: Research Article

Publication date: 2009-06-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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